update offroad video

Author: MsFriendly

_posts/2025-03-26-offroad.md

@@ -17,7 +17,7 @@ tags: Learning, Planning, Perception
 
 Off-road driving is an important robotics task with applications in agriculture, mining, exploration, and defense. While off-road driving has many similarities to driving in urban areas, a major difference is a lack of an obstacle/no obstacle dichotomy. That is, in off-road scenarios, not all objects are obstacles, and identifying which objects are traversable in a reliable way is critical.
 
-{% youtube 7t4EQj8BIdY %}
+{% youtube MBwNJh9Ghm4 %}
 
 Our research covers a wide range of topics that aim at expanding the robot’s capability as well as improving its robustness in challenging environments. We utilize modern machine learning techniques while eliminating the exhausting hand-labeling process. In specific, we explore self-supervised learning and learning-from-demonstration to understand the terrain traversability cost and vehicle dynamics from large-scale interaction data, online adapt the cost and dynamics model to overcome the out-of-distribution failures. Our system doesn’t require human labeled data, instead, it relies on its own experiences of interacting with the environment, while being aware of the uncertainty in each model, and online adapt the model in novel situations. We will explain our design philosophy in more detail and introduce the key components in the following sections. 
 
@@ -185,7 +185,7 @@ We adopt a philosophy that learned methods for off-road driving should be both s
 When operating at high speed, the vehicle requires good estimates beyond reactive range (> 30m), for more deliberate and safe navigation. Off-road vehicles will also often operate in new out-of-distribution environments (e.g., desert, forest) or even the same environments with different weather conditions (e.g., sunny vs. cloudy conditions). LiDAR is typically used to build a geometric understanding of the environment to generate traversability estimates. While LiDAR can provide accurate estimates robust to visual appearances, its noise grows as its range increases due to the sparsity of LiDAR points.
 On the contrary, camera-based methods output dense predictions at further distances. However, typical visual models rely on an immense amount of human-annotated data and perform poorly when the environmental appearance is out of training distribution.
 
-We therefore present ALTER, an Adaptive Long-range Traversibility EstimatoR that adapts on the drive to combine the best of LiDAR and camera to increase the reliable deployment envelope of our perception system, both in range and in environments.
+We therefore present [ALTER](https://arxiv.org/abs/2306.15226), an Adaptive Long-range Traversibility EstimatoR that adapts on the drive to combine the best of LiDAR and camera to increase the reliable deployment envelope of our perception system, both in range and in environments.
 Conceptually, we adapt a visual model online from new LiDAR measurements. First, our system labels near-range LiDAR measurements in 3D, then project the 3D labels to image space to produce pixel-wise labels. These labels are used to continuously train new visual models online. By rapidly learning from new measurements, our self-supervised, adaptive approach enables accurate long-range traversability prediction in novel environments without hand-labeling.
 
 We show, within 1 minute of combined data collection and training, our adaptive visual method produces up to 30% improvement in traversability estimation over LiDAR-only estimates and 60% improvement over visual models trained in another environment.
@@ -220,7 +220,7 @@ More field tests:
 
 To enable robust navigation in visually degraded environments, we developed a thermal voxel mapping module. Traditional thermal imaging provides valuable long-wave infrared information but suffers from low contrast and high noise, requiring specialized processing techniques for effective integration into a mapping pipeline.
 
-To address these challenges, we process 16-bit thermal images through a multi-step enhancement process, including histogram equalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), and bilateral filtering, validated in our previous work, FIReStereo. A colormap is then applied to the processed image for enhanced visualization. 
+To address these challenges, we process 16-bit thermal images through a multi-step enhancement process, including histogram equalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), and bilateral filtering, validated in our previous work, [FIReStereo](https://firestereo.github.io/). A colormap is then applied to the processed image for enhanced visualization. 
 
 For accurate sensor fusion, we obtain precise extrinsic calibration between thermal sensors and LiDAR using a customized cross-calibration board. Thermal features are then projected onto a voxel-based representation using point clouds from SuperOdometry, enabling real-time thermal mapping in complete darkness.